nocturia
发表于 2025-3-28 18:07:59
Textbook 2001ed ideas are illustrated by numerous figures, examples, and real-world applications. Fifteen years ago, nonlinear system identification was a field of several ad-hoc approaches, each applicable only to a very restricted class of systems. With the advent of neural networks, fuzzy models, and modern s
MUT
发表于 2025-3-28 20:04:18
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预防注射
发表于 2025-3-29 00:22:00
Textbook 2001nlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and the awareness of potential difficulties that may arise in practice. This book is self-contained in the sense that it requires merely basic knowledge of
enlist
发表于 2025-3-29 06:44:24
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Obituary
发表于 2025-3-29 08:07:08
Unsupervised Learning Techniquesa into another form, which hopefully can be better processed by the subsequent model. In this context, it is important to keep in mind that the desired output is actually available, and there may exist some efficient way to include this knowledge even into the preprocessing phase.
fetter
发表于 2025-3-29 13:21:26
Introduction to Static Modelspproaches. Chapters 13 and 14 introduce and extend the local linear neuro-fuzzy model architectures and in particular the local linear model tree (LOLIMOT) training algorithm. Finally, the main results of this part are summarized in Chap. 15.
Tortuous
发表于 2025-3-29 18:21:56
Introductiontem identification problem. Several modeling paradigms are reviewed in Sect. 1.3. Section 1.4 characterizes the purpose of this book and gives an outline with some reading suggestions. Finally, a few terminological issues are addressed in Sect. 1.5.
炸坏
发表于 2025-3-29 23:18:21
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极为愤怒
发表于 2025-3-30 00:42:38
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acrimony
发表于 2025-3-30 04:41:56
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